The present invention relates generally to radar and more specifically to an image processing and target detection process.
Moving targets are usually detected with ground moving target indication (GMTI) radars. However, very slow moving targets like tanks or dismounted troops might be below the minimum detectable velocity (MDV) of GMTI radars. In synthetic aperture radar (SAR) images, detection of moving targets is difficult because of target smear due to motion, which could make low-RCS targets fall below stationary ground clutter. Several techniques for SAR imaging of moving targets have been discussed in the following U.S. Patents, the disclosures of which are incorporated herein by reference:    U.S. Pat. No. 6,300,895, entitled Discreet radar detection method and system of implementation thereof, Carrara,    U.S. Pat. No. 5,898,399, entitled Subchirp processing method, Carrara,    U.S. Pat. No. 5,587,718, entitled Method for discovering and designating air targets, Iardella    U.S. Pat. No. 5,500,647, entitled Method for determining the rank of distance ambiguity of radar echoes, Carrara,    U.S. Pat. No. 4,972,194, entitled Method and device for compensating for the speed of clutter in a coherent doppler radar with variable blind speed, Carrara; and    U.S. Pat. No. 4,191,957, entitled Method of processing radar data from a rotating scene using a polar recording format, Walker.
The above-cited patents discuss prior art target detection techniques in SAR systems. These techniques require pre-detection, which, in turn, requires sufficient signal-to-stationary ground clutter ratio (SCR) and adequate MDV, and may result in a suboptimal performance vs. the informational content of the data (e.g. Cramer-Rao Bound). Extracting the maximum information from data is possible using adaptive, model-based approaches but in the past such approaches faced prohibitive combinatorial complexity. Parameters of the target models are unknown and have to be estimated from the image data. Combinatorial complexity is due to the need for having to consider a large number of combinations between multiple target models and the data.
In view of the foregoing there remains a need for a technique for detecting slow-moving targets in SAR images with low signal-to-clutter ratio and zero MDV without combinatorial complexity. The presentation will briefly summarize the difficulties encountered over the last 50 years related to the combinatorial complexity of computations. A new concept, dynamic logic, will be introduced along with an algorithm suitable for the detection of very slow-moving targets in SAR images. This new mathematical technique is inspired by the analysis of biological systems, like the human brain, which combines conceptual understanding with emotional evaluation and overcomes the combinatorial complexity of model-based techniques. The presentation will provide examples of detecting a single, slow moving target, and multiple moving targets.